Refactor introduction to clarify neural graphics model development and deployment process

Author: madeline-underwood

content/learning-paths/mobile-graphics-and-gaming/model-training-gym/1-introduction.md

@@ -16,9 +16,11 @@ Arm enables neural graphics through the [**Neural Graphics Development Kit**](ht
 
 At its core are the ML Extensions for Vulkan, which bring native ML inference into the GPU pipeline using structured compute graphs. These extensions (`VK_ARM_tensors` and `VK_ARM_data_graph`) allow real-time upscaling and similar effects to run efficiently alongside rendering tasks.
 
-The neural graphics models can be developed using well-known ML frameworks like PyTorch, and exported to deployment using Arm's hardware-aware pipeline. The workflow converts the model to `.vgf` via the TOSA intermediate representation, making it possible to do tailored model development for you game use-case. This Learning Path focuses on **Neural Super Sampling (NSS)** as the use case for training, evaluating, and deploying neural models using a toolkit called the [**Neural Graphics Model Gym**](https://github.com/arm/neural-graphics-model-gym). To learn more about NSS, you can check out the [resources on Hugging Face](https://huggingface.co/Arm/neural-super-sampling). Additonally, Arm has developed a set of Vulkan Samples to get started. Specifically, `.vgf` format is introduced in the `postprocessing_with_vgf` one. The Vulkan Samples and over-all developer resources for neural graphics is covered in the [introductory Learning Path](/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample).
 
-Starting in 2026, Arm GPUs will feature dedicated neural accelerators, optimized for low-latency inference in graphics workloads. To help developers get started early, Arm provides the ML Emulation Layers for Vulkan that simulate future hardware behavior, so you can build and test models now.
+You can develop neural graphics models using well-known ML frameworks like PyTorch, then export them for deployment with Arm's hardware-aware pipeline. The workflow converts your model to `.vgf` via the TOSA intermediate representation, making it possible to tailor model development for your game use case. In this Learning Path, you will focus on **Neural Super Sampling (NSS)** as the primary example for training, evaluating, and deploying neural models using the [**Neural Graphics Model Gym**](https://github.com/arm/neural-graphics-model-gym). To learn more about NSS, see the [resources on Hugging Face](https://huggingface.co/Arm/neural-super-sampling). Arm has also developed a set of Vulkan Samples to help you get started. The `.vgf` format is introduced in the `postprocessing_with_vgf` sample. For a broader overview of neural graphics developer resources including the Vulkan Samples, visit the [introductory Learning Path](/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/).
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+Starting in 2026, Arm GPUs will feature dedicated neural accelerators, optimized for low-latency inference in graphics workloads. To help you get started early, Arm provides the ML Emulation Layers for Vulkan that simulate future hardware behavior, so you can build and test models now.
 
 ## What is the Neural Graphics Model Gym?